Index terms:
There exist several analytical methods to measure the content of carbon in soil samples. The standard method for measuring the total carbon content is the dry combustion method, which is based on the oxidation of both organic and inorganic forms of carbon at very high temperatures (> 1000°C) in the presence of oxygen and copper oxide (Nelson & Sommers, 1996; Pansu & Gautheyrou, 2006). However, wet digestion methods, which are based on the reduction of Cr2O72- by organic compounds present in soil samples, are the most commonly used (Donagemma et al., 2011; Pansu & Gautheyrou, 2006). This is mostly due to three reasons. First, the implementation and maintenance costs associated with the use of the standard method are often high, what makes it unsuitable for small laboratories. Second, provided that the quantity of inorganic forms of carbon in soil samples is negligible, the accuracy of wet digestion methods is comparable to the standard method (Rheinheimer et al., 2008, Gatto et al. (2009)). Last, most soil classification and fertilizer recommendation systems were built using criteria based on soil organic carbon and/or organic matter content as measured using wet digestion methods (IUSS Working Group WRB, 2015; Santos et al., 2013; Silva et al., 2016).
Wet digestion methods have long been criticized because the ion Cr6+ is toxic to all forms of life (Pimentel et al., 2006; Rodella & Alcarde, 1994). As such, efforts have been made to stimulate the use of alternative methods. For example, the loss on ignition method, which is based on the quantification of the amount of mass lost by soil samples after oxidation of organic compounds at relatively high temperatures (> 300ºC) in the presence of oxygen (Pansu & Gautheyrou, 2006). In Brazil, this method was pointed as being an attractive alternative to measure the soil organic matter content – as used in fertilizer recommendation systems – due to the very low environmental hazards associated with it use (Brunetto et al., 2006; Escosteguy et al., 2007). With respect to the measurement of the organic carbon content, despite the standard dry combustion method being a natural alternative (Gatto et al., 2009), its widespread usage in Brazil has been hindered by the reasons mentioned above. Thus, alternative wet digestion methods that employ less Cr2O72- have been proposed to try to minimize their well known environmental hazards (Rheinheimer et al., 2008). These efforts helped shaping a complex scenario in which disparate analytical methods are employed in different studies and soil laboratories.
Comparing and using soil carbon data produced in different studies that employed disparate analytical methods require the development of harmonization mechanisms. Harmonization consists of converting the values of a soil property produced using an arbitrary analytical method to values that “look like” the values that would be produced if the reference analytical method had been used instead (Batjes et al., 2017). For example, the Van Bemmelen factor, 1.724, which relies on the strong assumption that soil organic matter is composed of 58% of carbon, is commonly used to transform organic carbon content to organic matter content (Pribyl, 2010). Similar conversion factors have already been estimated in Brazil, for example, to enable the adoption of the loss on ignition method as a substitute for the wet digestion method used to measure the organic matter content (Escosteguy et al., 2007), and to convert the organic carbon values produced via wet digestion methods to values of standard dry combustion methods (Gatto et al., 2009; Pereira et al., 2006; Rheinheimer et al., 2008).
In a broader sense, a conversion factor can be understood as being a pedotransfer function. A pedotransfer function consists of an empirical model that can be used to predict the values of a soil property from other soil properties that are easier or cheaper to measure or that are more readily available (McBratney et al., 2002). Applied to the context of soil carbon data, understanding conversion factors as being pedotransfer functions means taking the carbon content measured with disparate analytical methods as if they are different soil properties – which is specially true for total carbon, organic carbon, and organic matter content. These empirical models generally take the form of statistical models such as a linear regression. Conversion factors themselves are linear regression models based on the strong assumption that the regression line passes through the origin (Pribyl, 2010). In Brazil, additionally to conversion factors, many linear regression models have been developed to predict soil carbon and organic matter content from one another under a variety of soil conditions, e.g. granulometry, taxonomy, land use (Brunetto et al., 2006; Gatto et al., 2009; Pereira et al., 2006; Sampaio et al., 2012).
Studies on the comparison of methods for measuring soil carbon and organic matter content and to estimate conversion factors between analytical methods are absent in the recent Brazilian scientific literature. This means that pedotransfer functions may have been employed under very different soil conditions from those in which they were calibrated, i.e. for extrapolation. For example, in the southernmost Brazilian state, Rio Grande do Sul (RS), the most important studies on the subject, published about ten years ago, were more concerned with the Serra Gaúcha, Planalto, and Depressão Central regions, and with soil samples with an organic matter content lower than 15% (Brunetto et al., 2006; Escosteguy et al., 2007; Rheinheimer et al., 2008). The aim of the present study is to overcome these limitation by using a large set of soil samples covering various land uses, soil classes, sampling depths, and clay contents, sampled in different regions of RS, for which we have measured the carbon and organic matter contents using four popular analytical methods. The following section describes our approach for calibrating pedotransfer functions to predict the carbon and organic matter content. We then present the soil data and the analytical methods employed. Later sections are reserved for the results and some recommendations for future studies.
We evaluated the methods of estimating the organic carbon/matter content in n = 105 air-dried < 2 mm-soil samples collected at 27 sites in the State of Rio Grande do Sul (RS), Brazil. The samples cover eleven soil groups (Acrisols, Alisols, Cambisols, Ferralsols, Fluvisols, Gleysols, Leptosols, Nitisols, Planosols, Regosols, and Vertisols) derived from several parent materials (sedimentary, volcanic, and metamorphic). The clay content of the soil samples varies between 40 and 800 g kg-1 (Figure 3.1). The mineralogical assemblage is diverse (quartz, iron oxide, and 2:1 and 1:1 phyllosilicates). Several land use types are represented: rangeland, farmland, fallow land, plantation (Eucalyptus sp.) and native forests. The climate of the sampling sites varies from humid subtropical to temperate climate, both without a defined dry season. Mean annual precipitation across RS ranges from approximately 1300 to 2000 mm. The mean annual temperature across RS is about 18°C, whichever is colder in higher sites (> 800 m a.s.l.) and warmer in lower sites (< 200 m a.s.l.). Soil samples collection procedures are described elsewhere (Casali et al., 2006, Dick et al. (2008), Britzke (2010), Potes et al. (2010), Vieira (2010), Samuel-Rosa et al. (2013b), Samuel-Rosa et al. (2013a)). We grounded the soil samples in an agate mortar (< 1 mm) and stored them in microtubes (Eppendorf type) in the dark at room temperature until analysis.
Figure 3.1: Key characteristics of the soil samples, such as the clay content and sampling depth, and their sampling sites, such as the soil classification and type of land use and occupation.
Sample aliquots of 50 to 60 mg [ESTES VALORES ESTÃO CORRETOS???] in titanium capsules were used to estimate the total soil organic carbon content in an elementary CHNS analyzer (FlashEA 1112, Thermo Finnigan, Milan, Italy).
Sample aliquots of 0.050 to 0.500 g were placed in glass digestion tubes (80 mL). The amount of sample used varied according to the SOM content estimated through visual interpretation of soil color. The correlation between SOM content and sample aliquot used as calculated in the input training dataset is r = -0.8436. Every digestion tube received an aliquot of 10 mL of 0.067 mol L-1 sulfocromic solution [K2Cr2O7 + H2SO4] and a small reflux funnel to avoid loss of reagent during digestion. A digestion block with capacity for 40 samples was used: 36 tubes with soil sample + 3 tubes with blank + 1 tube with sulfuric acid [H2SO4] and a thermometer for temperature measurement. Digestion at 150ºC last 30 min. Three blanks were prepared and set aside at room temperature to estimate the loss of reagent due to heat in the digestion block. After digestion the tubes were set aside at room temperature to cool down. Next, we transfered the solution to Erlenmeyer flasks (250 mL) with 60 mL of distilled water and 2 mL of concentrated orthophosphoric acid [H3PO4] and 3 drops of 1% diphenylamine. The solution was titrated using 0.1 mol L-1 ammonium ferrous sulfate [Fe(NH4)2(SO4])2.6H2O] until persistent green color. This method was originaly adapted from Mebius (1960) and Yeomans & Bremner (1988).
Adapted from Tedesco et al. (1995). Sample aliquots of 2.5 mL were placed in Erlenmeyer flasks (50mL). Every Erlenmeyer flask received an aliquot of 15 mL of 0.067 mol L-1 sulfocromic solution [Na2Cr2O7 + H2SO4]. The flasks were heated in a water bath at 75 to 80°C during 30 min and shaken for 5 min. A water aliquot of 15 mL was added to the flask and let overnight (15 to 18 hours). In the next day, we sampled an aliquot of 3.0 mL to a small cup with 3.0 mL of distilled water. The supernatant absorbance was measured at 645 nm. We weighted 2.5 mL of each soil sample to calculate the sample density and transform the results to a weight basis.
Sample aliquots of 300 to 500 mg in hand-made aluminum capsules (20 mg) (oven dried at 360°C) were placed in an oven at 105°C for 24 hours. We varied the sample aliquot used due to sample availability what had no noticeable effect on the analytical results as showed by linear correlation analysis (data not shown). Next, we placed the oven-dried samples in a desiccator containing silica gel and weighted after they had cooled down. The amount of mass lost between the first and second weightings was used to calculate the water content in the sample. Next, we placed the samples in an oven at 360ºC for 2 hours. Next, samples were placed in a desiccator containing silica gel and weighted after they had cooled down. The organic matter content in the samples was calculated from the mass lost between the second and third weightings. This method was adapted from Schulte & Hopkins (1996).
Figure 5.1: Carbon and organic matter content in soil samples according to the four analythical methods.
Figure 5.2: Scatter plot matrix of the carbon and organic matter content measured using four different analythical methods and their relation to the total clay content. The solid line represents a perfect 1:1 linear relation, while the dashed line is the observed empirical linear relation between variables.
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